Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A system comprising: a memory; one or more processors communicatively coupled to an agricultural implement and a display device and configured to perform: automatically obtaining, as a machine coupled to the agricultural implement is traveling in an agricultural field, raw data measured in real time from the agricultural field by sensors on the agricultural implement or the machine; automatically identifying an agricultural task being performed by the agricultural implement from the raw data; and automatically generating, in response to the identifying, maps showing a result of prior performance of the agricultural task in the agricultural field; transmitting the maps through a communication network to a display device of the machine as the agricultural implement continues traveling in the agricultural field.
Precision agriculture systems monitor and optimize farming operations using real-time sensor data. A key challenge is efficiently analyzing field conditions and historical performance to guide current tasks. This system addresses this by integrating sensors on agricultural implements or attached machines to collect real-time data as they traverse a field. The system processes this raw data to automatically determine the ongoing agricultural task, such as planting, fertilizing, or harvesting. Based on this identification, it generates maps depicting prior performance of the same task in the field, such as yield maps or application rate maps. These maps are transmitted wirelessly to a display device on the machine or implement, allowing operators to view historical context while actively working. The system enables real-time decision-making by providing immediate access to relevant historical data, improving task accuracy and efficiency. The solution eliminates manual data processing and enhances situational awareness for farmers and operators.
2. The system of claim 1 , further comprising the agricultural implement or the display device.
Agricultural systems often require precise monitoring and control of implement operations to optimize efficiency and productivity. Existing systems may lack integrated solutions for real-time data visualization and implement adjustments, leading to suboptimal performance. This invention addresses these challenges by providing an agricultural system with enhanced data processing and user interface capabilities. The system includes a controller configured to receive sensor data from an agricultural implement, such as a planter or sprayer, and process this data to generate operational insights. A display device, such as a touchscreen or mobile device, is used to present this data in a user-friendly format, allowing operators to monitor implement performance in real time. The system may also include the agricultural implement itself, ensuring seamless integration between data collection, processing, and visualization. By providing real-time feedback and control options, the system enables operators to make informed decisions, improving efficiency and reducing waste. The invention enhances existing agricultural systems by combining data processing, visualization, and implement control into a unified solution.
3. The system of claim 1 , the one or more processors are further configured to perform searching the raw data for data having a characteristic associated with location component data including at least one of an identifying portion associated with location data and a data unit size, length or frequency associated with location data.
This invention relates to a data processing system designed to identify and extract location-related data from raw data sources. The system addresses the challenge of efficiently locating and processing relevant location data within large, unstructured datasets, which is critical for applications such as geospatial analysis, navigation, and location-based services. The system includes one or more processors configured to analyze raw data to detect specific characteristics indicative of location data. These characteristics include an identifying portion, such as metadata or tags explicitly associated with location information, and structural features like data unit size, length, or frequency that are commonly found in location data formats. For example, the system may recognize patterns such as GPS coordinates, geohashes, or other standardized location data formats based on their size, length, or frequency of occurrence. Additionally, the system may employ filtering mechanisms to refine the search, ensuring that only relevant location data is extracted while minimizing false positives. This process enhances the accuracy and efficiency of location data extraction, making it suitable for real-time applications where timely and precise location information is essential. The system's ability to dynamically adapt to different data structures and formats further improves its versatility across various data sources.
4. The system of claim 1 , wherein the one or more processors are further configured to perform sending a query to a machine network of the machine or an implement network of the agricultural implement for requesting location information or identification of location information associated with the raw data.
This invention relates to agricultural data processing systems that collect and analyze raw data from machines or implements in agricultural operations. The problem addressed is the difficulty in accurately associating raw data with specific locations in a field, which is critical for precision agriculture applications such as yield mapping, soil analysis, and variable-rate application of inputs. The system includes one or more processors configured to process raw data collected from agricultural machines or implements. The processors are further configured to send a query to either a machine network associated with the agricultural machine or an implement network associated with the agricultural implement. The query requests location information or the identification of location information associated with the raw data. This allows the system to correlate the raw data with precise geographic coordinates, enabling accurate spatial analysis and decision-making in agricultural operations. The system may also include a user interface for displaying the processed data and its associated location information, as well as a data storage component for storing the raw and processed data. The system may further include a communication interface for transmitting the processed data to other systems or devices, such as cloud-based analytics platforms or farm management software. The system may also include a data validation component to ensure the accuracy and reliability of the location information.
5. The system of claim 1 , wherein the raw data includes at least one of seed sensor data, yield data, and liquid application rate data.
Agricultural data collection and analysis systems are used to monitor and optimize farming operations by gathering and processing various types of field data. A key challenge in these systems is efficiently integrating diverse data sources to provide actionable insights for precision agriculture. This invention describes a system that collects and processes raw agricultural data, including seed sensor data, yield data, and liquid application rate data, to enhance decision-making in farming operations. The system captures real-time or historical data from sensors and equipment deployed in agricultural fields, such as seed placement sensors, yield monitors, and liquid applicators. By analyzing this data, the system can identify patterns, detect anomalies, and generate recommendations for optimizing planting, irrigation, and fertilizer application. The integration of multiple data types allows for a comprehensive assessment of field conditions, enabling farmers to adjust practices dynamically for improved crop yield and resource efficiency. The system may also include data processing modules to clean, normalize, and correlate the different data streams, ensuring accurate and reliable insights. This approach supports precision agriculture by providing a unified view of field performance and facilitating data-driven decision-making.
6. The system of claim 3 , wherein the location component data includes at least one of GPS data and real-time kinematics data.
A system for tracking and determining the precise location of an object or device incorporates a location component that generates location component data. This data includes at least one of GPS (Global Positioning System) data or real-time kinematics (RTK) data, which are used to enhance the accuracy and reliability of position determination. The system may also include a communication module for transmitting the location data to a remote server or another device, and a processing unit that analyzes the location data to provide real-time or historical positioning information. The use of GPS data provides a basic level of accuracy, while RTK data offers centimeter-level precision by correcting GPS signals using reference stations. This system is particularly useful in applications requiring high-accuracy positioning, such as surveying, autonomous vehicle navigation, and asset tracking. The integration of multiple location data sources ensures robustness against signal interference and environmental factors, improving overall system performance.
7. The system of claim 1 , wherein the display device is removable from the machine.
A system for integrating a display device with a machine, where the display device is detachable from the machine. The display device is designed to provide visual information related to the machine's operation, such as status indicators, diagnostic data, or user interface elements. The system includes a mounting mechanism that allows the display device to be securely attached to the machine during use and easily removed when not needed. The display device may communicate with the machine wirelessly or through a wired connection, ensuring seamless data transfer even when detached. This removable feature enhances flexibility, allowing the display device to be repositioned, shared among multiple machines, or used independently for other purposes. The system may also include a power management feature to conserve energy when the display device is detached, such as automatically entering a low-power mode or disconnecting from the machine's power supply. The display device may be a touchscreen or a non-touch display, depending on the application. This design is particularly useful in industrial, medical, or consumer electronics where portability and adaptability are important.
8. A method, comprising: automatically obtaining, as a machine coupled to an agricultural implement is traveling in an agricultural field, raw data measured in real time from the agricultural field by sensors on the agricultural implement or the machine; automatically identifying an agricultural task being performed by the agricultural implement from the raw data; and automatically generating, in response to the identifying, maps showing a result of prior performance of the agricultural task in the agricultural field; transmitting the maps through a communication network to a display device of the machine as the agricultural implement continues traveling in the agricultural field.
This invention relates to precision agriculture, specifically a system for real-time monitoring and mapping of agricultural tasks. The problem addressed is the lack of immediate feedback on field operations, which can lead to inefficiencies in crop management. The solution involves a machine connected to an agricultural implement (e.g., a tractor, planter, or sprayer) that collects real-time sensor data from the field as the implement moves. Sensors may measure soil conditions, crop health, or application rates of inputs like fertilizers or pesticides. The system automatically analyzes this raw data to determine the specific agricultural task being performed (e.g., planting, spraying, or harvesting). Based on this identification, the system generates maps showing the results of prior executions of the same task in the field. These maps are transmitted wirelessly to a display device on the machine, allowing operators to view historical performance data while continuing fieldwork. This enables real-time adjustments to optimize operations, such as correcting uneven fertilizer distribution or identifying areas needing re-treatment. The system improves decision-making by providing immediate, context-aware insights derived from historical and current field data.
9. The method of claim 8 , further comprising automatically identifying the agricultural implement from the raw data.
Agricultural machinery monitoring systems often struggle to accurately identify and track different implements attached to tractors or other vehicles in real-time. This can lead to inefficiencies in data collection, automation, and decision-making. The invention addresses this problem by automatically identifying agricultural implements from raw sensor data collected during field operations. The system processes this data—such as images, sensor readings, or positional data—to distinguish between different implements (e.g., plows, seeders, or harvesters) without manual input. This identification is integrated into a broader monitoring system that tracks implement performance, usage, and maintenance needs. The method ensures accurate, real-time recognition of implements, improving operational efficiency and reducing errors in data logging. By automating implement identification, the system enhances precision agriculture by enabling better fleet management, predictive maintenance, and task automation. The solution is particularly useful in large-scale farming operations where multiple implements are frequently swapped or used in sequence.
10. The method of claim 8 , wherein the identified agricultural task comprises harvesting and the raw data includes sensor data and location data to generate a yield map.
Agricultural operations often require precise data collection to optimize tasks like harvesting. Traditional methods lack real-time data integration, leading to inefficiencies in yield assessment and resource allocation. This invention addresses the problem by providing a method for identifying agricultural tasks, such as harvesting, and processing raw data to generate actionable insights. The method involves collecting sensor data from agricultural equipment, such as combine harvesters, along with location data from GPS or similar systems. The sensor data may include measurements like crop yield, moisture levels, and equipment performance metrics. The location data correlates these measurements to specific field locations, enabling the creation of a detailed yield map. This map visually represents variations in crop yield across different areas of the field, allowing farmers to analyze productivity patterns and make informed decisions. By integrating sensor and location data, the method provides a comprehensive yield map that supports precision agriculture. Farmers can use this information to adjust harvesting strategies, optimize resource use, and improve overall crop management. The system enhances efficiency by automating data collection and analysis, reducing manual effort and improving accuracy in yield assessment. This approach supports sustainable farming practices by enabling targeted interventions based on real-time data.
11. The method of claim 8 , wherein the identified agricultural task comprises planting and the raw data includes sensor data and location data to generate a planting map.
Agricultural operations often require precise planting to optimize crop yield and resource efficiency. Traditional methods rely on manual planning or basic GPS guidance, which may lack real-time adaptability to field conditions. This invention addresses the need for automated, data-driven planting decisions by analyzing raw data from sensors and location systems to generate a planting map. The method involves collecting sensor data, such as soil moisture, temperature, or nutrient levels, along with location data from GPS or similar systems. This raw data is processed to identify optimal planting locations, accounting for environmental factors and field variability. The system then generates a planting map that guides agricultural machinery, ensuring seeds or seedlings are placed in the most suitable areas. This approach improves planting accuracy, reduces waste, and enhances crop uniformity. The invention may also integrate with other agricultural tasks, such as soil preparation or irrigation, by using the same data analysis framework. By leveraging real-time data, the system adapts to changing conditions, ensuring efficient and precise planting operations. This method is particularly useful in large-scale farming, where manual planning is impractical and variability across fields is significant. The result is a more automated, data-centric approach to planting that maximizes yield potential while minimizing resource use.
12. The method of claim 8 , wherein automatically identifying the agricultural task comprises searching the raw data for data including at least one of an identifying portion associated with task information and a data unit size, length or frequency associated with task data.
This invention relates to agricultural task identification using raw data analysis. The problem addressed is the need to automatically and accurately determine specific agricultural tasks from raw data collected during farming operations. The solution involves analyzing raw data to identify patterns or markers that correspond to particular tasks, such as planting, harvesting, or irrigation, without requiring manual input or pre-labeled datasets. The method processes raw data from agricultural equipment or sensors to detect task-related information. This includes searching for identifying portions within the data that correlate with task metadata, such as timestamps, equipment identifiers, or operational parameters. Additionally, the method examines data unit characteristics like size, length, or frequency to distinguish between different tasks. For example, a specific data pattern or frequency may indicate a harvesting operation, while a different pattern may correspond to soil preparation. The approach improves efficiency by reducing the need for human intervention in task classification and enables real-time or near-real-time task monitoring. By leveraging raw data features, the system can adapt to various agricultural scenarios without extensive prior training or configuration. This enhances automation in precision agriculture, allowing for better resource management and operational insights.
13. The method of claim 12 , wherein the task information includes an implement identifier that is associated with at least one of implement types, makes, or model.
This invention relates to a system for managing agricultural tasks, specifically improving task assignment and tracking in farming operations. The problem addressed is the inefficiency in assigning and monitoring agricultural tasks due to lack of detailed implement information, leading to mismatched equipment usage, delays, and reduced productivity. The method involves collecting and processing task information for agricultural operations, where the task information includes an implement identifier. This identifier is linked to specific details about the implement, such as its type (e.g., plow, harvester), make, and model. By associating tasks with these identifiers, the system ensures that the correct implements are used for each task, optimizing equipment allocation and reducing errors. The method also allows for tracking implement usage, maintenance needs, and performance metrics, improving overall farm management. The system may integrate with existing farm management software or IoT-enabled implements to automatically capture implement data. This ensures real-time updates and accurate task assignments. The method enhances operational efficiency by preventing mismatches between tasks and implements, reducing downtime, and enabling better resource planning. The solution is particularly useful in large-scale farming operations where multiple implements and tasks must be coordinated effectively.
14. The method of claim 12 , wherein the raw data includes controller or sensor signals having a frequency, wherein automatically identifying the agricultural task comprises searching a database associating frequency of controller or sensor pulses with a type of agricultural application.
This invention relates to agricultural machinery automation, specifically identifying agricultural tasks based on raw data from controllers or sensors. The problem addressed is the need for automated task recognition in agricultural equipment to improve efficiency and reduce manual intervention. The method involves analyzing raw data from controllers or sensors, which generate signals with specific frequencies corresponding to different agricultural tasks. The system automatically identifies the task by searching a database that maps the frequency of controller or sensor pulses to specific types of agricultural applications. For example, a particular frequency pattern in a seed planter's sensor signals may indicate planting, while a different pattern may indicate spraying. The database is pre-populated with known frequency-task associations, allowing the system to match incoming signals to the correct task. This approach enables real-time task identification without requiring additional hardware or complex signal processing, improving operational accuracy and automation in agricultural machinery. The method is particularly useful for integrating with existing equipment, as it leverages existing controller and sensor data rather than requiring new sensors or modifications.
15. The method of claim 8 , further comprising: sending a query to a machine network of the machine or an implement network of the agricultural implement for requesting location information or identification of location information.
Agricultural machinery and implements often require precise location tracking to optimize operations, such as planting, harvesting, or field mapping. Existing systems may lack real-time or accurate location data, leading to inefficiencies in field management. This invention addresses the need for reliable location tracking by integrating a method that retrieves location information from a machine network or an agricultural implement network. The method involves sending a query to either the machine's internal network or the implement's network to request location data or identify where such data is stored. This ensures that the system can dynamically access the most relevant location information, improving operational accuracy and decision-making. The machine network may include sensors, GPS modules, or other onboard systems that provide real-time positioning. Similarly, the implement network may include attached tools or devices that contribute to location tracking. By querying these networks, the system can consolidate and utilize location data effectively, enhancing agricultural productivity and resource management. This approach ensures that location information is readily available when needed, reducing delays and errors in field operations.
16. A method of claim 8 , further comprising: initiating a software application; determining, with a processing system, a communication unit, or the display device, whether at least one of automatic field identification and automatic task identification occurs based on initiation of the software application; displaying on a graphical user interface of the display device at least one of the determined automatic field identification and the automatic task identification if at least one of automatic field identification and automatic task identification occurs; and receiving input for correcting the automatic field identification with at least one alternative field if correction is needed when the automatic field identification occurs.
This invention relates to software applications that automatically identify fields or tasks upon initiation, reducing manual input errors and improving user efficiency. The method involves launching a software application and automatically determining whether field or task identification should occur based on predefined criteria. If automatic identification is triggered, the system displays the identified fields or tasks on a graphical user interface. Users can then review and correct any inaccuracies by selecting alternative fields when necessary. The system may use a processing system, communication unit, or display device to perform these functions. This approach streamlines data entry and task management by minimizing manual input while allowing for user verification and correction. The invention is particularly useful in applications where repetitive or structured data entry is common, such as forms, spreadsheets, or workflow management systems. By automating initial identification, the system reduces cognitive load and speeds up workflows while maintaining accuracy through user oversight.
17. The method of claim 16 , further comprising: receiving input for correcting the automatic task identification with at least one alternative task if correction is needed when the automatic task identification occurs.
This invention relates to systems for automatically identifying tasks from user input and improving the accuracy of such identifications. The problem addressed is the potential for errors in automatic task recognition, which can lead to incorrect task execution or user frustration. The invention provides a method for correcting these errors by allowing users to provide alternative task suggestions when the system's automatic identification is incorrect. The method involves receiving user input, such as text or voice commands, and automatically identifying a task based on that input. If the system determines that the automatic identification may be incorrect, it prompts the user to provide at least one alternative task. The system then processes this feedback to refine its task recognition capabilities, improving future accuracy. The method may also include analyzing the user's input to determine whether correction is needed, such as by detecting ambiguity or low-confidence matches. Additionally, the system may store corrected tasks to enhance its learning model, ensuring continuous improvement in task identification. This approach ensures that even when the system's initial identification is flawed, the user can easily correct it, maintaining efficiency and accuracy in task execution. The method is particularly useful in applications like virtual assistants, automation tools, and smart home systems where precise task recognition is critical.
18. The method of claim 16 , further comprising: waiting for a subsequent determination of whether at least one of automatic field identification and automatic task identification occurs when no automatic field or task identification is initially determined to have occurred.
This invention relates to systems for automatically identifying fields and tasks in data processing, particularly in scenarios where initial automatic identification fails. The technology addresses the problem of incomplete or inaccurate field and task recognition in automated data processing workflows, which can lead to errors, inefficiencies, or the need for manual intervention. The method involves an initial attempt to automatically identify fields or tasks within a dataset or workflow. If this initial attempt fails to produce a valid identification, the system enters a waiting state. During this state, the system monitors for subsequent conditions or triggers that may enable successful automatic identification. Once such conditions are met, the system reattempts the identification process. This approach ensures that the system does not prematurely conclude that identification is impossible, allowing for dynamic adjustments based on evolving data or contextual factors. The method may be applied in various domains, including document processing, form automation, or task scheduling systems, where accurate field and task recognition is critical for efficient operation. The waiting period may involve monitoring for changes in input data, user interactions, or system states that could facilitate successful identification. By incorporating this adaptive waiting mechanism, the system improves reliability and reduces the need for manual corrections.
19. The method of claim 16 , further comprising: generating alternative fields for the automatic field identification if appropriate; and sending the alternative fields to the display device for display on the graphical user interface.
This invention relates to automatic field identification in data processing systems, particularly for improving the accuracy and usability of field recognition in graphical user interfaces. The problem addressed is the difficulty in accurately identifying and displaying fields in data entry or processing applications, where manual correction or selection of fields is time-consuming and error-prone. The invention provides a method for automatically identifying fields in a dataset or input form and presenting them in a graphical user interface. The method includes analyzing input data to determine field boundaries, types, and relationships, then displaying the identified fields for user interaction. If the automatic identification process is uncertain or produces ambiguous results, the system generates alternative field options based on probabilistic or heuristic analysis. These alternative fields are sent to the display device for presentation in the graphical user interface, allowing users to select the correct field from the provided options. This reduces manual effort and improves the efficiency of data processing tasks by providing intelligent suggestions when automatic field recognition is inconclusive. The invention enhances user experience by minimizing errors and streamlining data entry or manipulation workflows.
20. The method of claim 16 , further comprising: generating alternative tasks for the automatic task identification if appropriate; and sending the alternative tasks to the display device for display on the graphical user interface.
This invention relates to systems for automatically identifying and managing tasks in a computing environment. The problem addressed is the inefficiency of manual task identification and the lack of flexibility in task management, which can lead to errors and delays in workflow execution. The method involves automatically identifying tasks from input data, such as user commands or system events, and generating a set of potential tasks based on the identified input. If the initial task identification is deemed inappropriate, the system generates alternative tasks that better match the user's intent or system requirements. These alternative tasks are then presented to the user via a graphical user interface, allowing for selection or further refinement. The system may also prioritize tasks based on predefined criteria, such as urgency or relevance, to optimize workflow efficiency. The method ensures that task identification is dynamic and adaptable, reducing the need for manual intervention while improving accuracy and responsiveness in task execution. This approach is particularly useful in environments where tasks must be processed quickly and accurately, such as in automated workflows or user-assistance systems.
Unknown
October 27, 2020
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